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Reinforcement Learning Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep ...

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How much do reinforcement learning jobs pay per year?

As of Sep 6, 2026, the average yearly pay for reinforcement learning in the United States is $58,347.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,500.00 and $68,000.00 per year, depending on experience, location, and employer.

What is a reinforcement learning?

A Reinforcement Learning (RL) job involves designing, developing, and optimizing algorithms that enable machines to learn from interactions with their environment. RL professionals work on applications in robotics, finance, gaming, and autonomous systems, leveraging techniques like deep reinforcement learning and policy optimization. Responsibilities often include researching new models, implementing RL algorithms, and improving AI performance. Strong programming skills, knowledge of machine learning frameworks, and an understanding of mathematical concepts like probability and optimization are essential.

What does a reinforcement learning professional do?

A typical day for a Reinforcement Learning professional involves designing and implementing learning algorithms, running experiments, analyzing data, and iterating on models to improve performance. You might collaborate closely with data scientists, software engineers, and product managers to integrate your solutions into broader systems or products. Regular activities also include reading recent research literature and participating in team meetings to discuss progress and obstacles. This dynamic role often balances deep technical work with teamwork to drive innovative applications in areas such as robotics, recommendation systems, or autonomous systems.

What are the key skills and qualifications needed to thrive in the reinforcement learning position?

To thrive in a Reinforcement Learning role, you need a solid background in mathematics, statistics, machine learning, and programming (commonly with Python), typically supported by a relevant degree such as in computer science or engineering. Experience with frameworks like TensorFlow, PyTorch, OpenAI Gym, and familiarity with large-scale computing systems are highly valued. Strong problem-solving abilities, curiosity, and effective collaboration and communication skills help you excel in multidisciplinary research and project teams. These capabilities are crucial for designing, implementing, and refining complex algorithms that learn from interaction to solve real-world problems.

What can you do with reinforcement learning?

Reinforcement learning is used in roles such as reinforcement learning engineer or researcher to develop algorithms that enable systems to learn optimal actions through trial and error. It is applied in areas like robotics, game playing, autonomous vehicles, and recommendation systems, often requiring skills in programming, data analysis, and understanding of machine learning frameworks. Professionals in this field design, train, and evaluate models to improve decision-making processes in complex environments.
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Reinforcement Learning jobs near you
Infographic showing various Reinforcement Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $58,347 per year, or $28.1 per hour.

Research Scientist - Reinforcement Learning

MBZUAI (Mohamed bin Zayed University of Artificial Intelligence)

Sunnyvale, CA • On-site

Full-time

Re-posted 11 days ago

Key responsibilities

  • Develop novel reinforcement learning approaches for large-scale self-play, agentic tasks, and proactive environment learning.

  • Contribute to large-scale reinforcement learning training and inference frameworks, including data curation, model architecture, and algorithm design.

  • Collaborate with internal and external partners and contribute to technical reports and research publications.

Job description

Job Summary:
The Institute of Foundation Models is a dedicated research lab focused on advancing research and building capabilities in foundation models. As a Research Scientist in the Reinforcement Learning team, you will develop novel approaches to reinforcement learning, contribute to large-scale training infrastructure, and maintain a productive research portfolio while collaborating with internal and external partners.
Responsibilities:
• Develop novel research toward massive scale self-play for foundation model training, agentic tasks, and imbuing models with the capability to proactively learn from its environment.
• Initiate and pursue novel reinforcement learning algorithmic approaches to define and drive emergent capabilities in Foundation Models.
• Full-stack engineering from data curation, model architecture and algorithm design, to final production of models for end-users using high quality (documented, tested, maintainable) code.
• Contribute to technical reports and research publications.
• Represent MBZUAI at industry conferences and events, showcasing the institution’s technology and deep learning capabilities and establishing MBZUAI as a global leader in AI research and innovation.
• Proactively engage with the open-source community.
• Contribute to large-scale reinforcement learning training and inference frameworks.
• Facilitate internal and external collaboration
Qualifications:
Required:
• MSc/MEng or PhD Degree (or equivalent experience) in Machine Learning, Computer Science or related fields.
• 3+ years of hands-on experience with reinforcement learning.
• Demonstrated ability to independently identify limitations of current practice (internal and external), formulate and enact solution strategies for improvement.
• Proactive mindset with the ability to identify impactful research questions and execute on them with minimal supervision.
• Strong Python development skills with a focus on research-grade code and scalable data pipelines.
• Practical experience implementing complex mathematical concepts into reliable, well-documented code.
• Experience applying novel RL algorithms to practical applications.
• Strong experience contributing to academic and/or open-source research through publication, GitHub contributions, or professional presentations.
• Strong communication and collaboration skills for effective cross-functional work.
Preferred:
• Strong systems and engineering expertise in deep learning frameworks such as PyTorch, Jax, etc.
• Experience in large-scale model training (LLMs or Diffusion Models) on large clusters.
• Familiarity with current RL+LLM training libraries.
• Experience training policies in self-play, possibly demonstrated by publication, blog post, public code.
• Experience working with Diffusion Models in RL, possibly demonstrated by publication, blog post, public code.
• Strong publication record in leading AI and RL venues (e.g.ICLR, ICML, NeurIPS, RLC, JMLR, TMLR).
• Familiarity with performance constraints in production environments and the trade-offs in model design and execution.
• Prior contributions to open-source ML research or data tools.
• Demonstrated ability to solve complex system-level challenges and debug failures across training/inference stack (e.g. memory issues, deadlocks, I/O bottlenecks, multi-node communication failures).
Company:
Official account of Mohamed bin Zayed University of Artificial Intelligence. Dedicated to research, innovation, and empowering brilliant minds in AI. Founded in 2019, the company is headquartered in Abu Dhabi, ARE, with a team of 51-200 employees. The company is currently Growth Stage.

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